Software & Data Downloads — MetaLIC
Meta-Learning State Space Models for training and evaluating meta-learning for system identification and control via neural state-space models.
PyTorch implementation with Bouc-Wen nonlinear system identification benchmark using meta-learned neural state-space models for rapid adaptation.
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MERL Contacts
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Related Publications
- , "Meta-Learning of Neural State-Space Models Using Data From Similar Systems", World Congress of the International Federation of Automatic Control (IFAC), DOI: 10.1016/j.ifacol.2023.10.1843, July 2023.
BibTeX TR2023-087 PDF Software- @inproceedings{Chakrabarty2023jul,
- author = {Chakrabarty, Ankush and Wichern, Gordon and Laughman, Christopher R.},
- title = {Meta-Learning of Neural State-Space Models Using Data From Similar Systems},
- booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
- year = 2023,
- month = jul,
- doi = {10.1016/j.ifacol.2023.10.1843},
- url = {https://www.merl.com/publications/TR2023-087}
- }
- , "Meta-Learning of Neural State-Space Models Using Data From Similar Systems", World Congress of the International Federation of Automatic Control (IFAC), DOI: 10.1016/j.ifacol.2023.10.1843, July 2023.
Software & Data Downloads
Access software at https://github.com/merlresearch/MetaLIC.